OpenRaiser/Intern-Atlas huggingface dataset:
Open set label noise learning with robust sample selection and margin-guided module
Venue: arXiv cs.CV 2025
Year: 2025

In recent years, the remarkable success of deep neural networks (DNNs) in computer vision is largely due to large-scale, high-quality labeled datasets. Training directly on real-world datasets with label noise may result in overfitting. The traditional method is limited to deal with closed set label noise, where noisy training data has true class labels within the known label space. However, there are some real-world datasets containing open set label noise, which means that some samples belong to an unknown class outside the known label space. To address the open set label noise problem, we introduce a method based on Robust Sample Selection and Margin-Guided Module (RSS-MGM). Firstly, unlike the prior clean sample selection approach, which only select a limited number of clean samples, a robust sample selection module combines small loss selection or high-confidence sample selection to obtain more clean samples. Secondly, to efficiently distinguish open set label noise and closed set ones, margin functions are designed to filter open-set data and closed set data. Thirdly, different processing methods are selected for different types of samples in order to fully utilize the data's prior information and optimize the whole model. Furthermore, extensive experimental results with noisy labeled data from benchmark datasets and real-world datasets, such as CIFAR-100N-C, CIFAR80N-O, WebFG-469, and Food101N, indicate that our approach outperforms many state-of-the-art label noise learning methods. Especially, it can more accurately divide open set label noise samples and closed set ones.
Separate Source Channel Coding Is Still What You Need: An LLM-based Rethinking
Venue: arXiv cs.IT 2025
Year: 2025

Along with the proliferating research interest in Semantic Communication (SemCom), Joint Source Channel Coding (JSCC) has dominated the attention due to the widely assumed existence in efficiently delivering information semantics. Nevertheless, this paper challenges the conventional JSCC paradigm, and advocates for adoption of Separate Source Channel Coding (SSCC) to enjoy the underlying more degree of freedom for optimization. We demonstrate that SSCC, after leveraging the strengths of Large Language Model (LLM) for source coding and Error Correction Code Transformer (ECCT) complemented for channel decoding, offers superior performance over JSCC. Our proposed framework also effectively highlights the compatibility challenges between SemCom approaches and digital communication systems, particularly concerning the resource costs associated with the transmission of high precision floating point numbers. Through comprehensive evaluations, we establish that empowered by LLM-based compression and ECCT-enhanced error correction, SSCC remains a viable and effective solution for modern communication systems. In other words, separate source and channel coding is still what we need!
VerifBFL: Leveraging zk-SNARKs for A Verifiable Blockchained Federated Learning
Venue: arXiv cs.CR 2025
Year: 2025

Blockchain-based Federated Learning (FL) is an emerging decentralized machine learning paradigm that enables model training without relying on a central server. Although some BFL frameworks are considered privacy-preserving, they are still vulnerable to various attacks, including inference and model poisoning. Additionally, most of these solutions employ strong trust assumptions among all participating entities or introduce incentive mechanisms to encourage collaboration, making them susceptible to multiple security flaws. This work presents VerifBFL, a trustless, privacy-preserving, and verifiable federated learning framework that integrates blockchain technology and cryptographic protocols. By employing zero-knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARKs) and incrementally verifiable computation (IVC), VerifBFL ensures the verifiability of both local training and aggregation processes. The proofs of training and aggregation are verified on-chain, guaranteeing the integrity and auditability of each participant's contributions. To protect training data from inference attacks, VerifBFL leverages differential privacy. Finally, to demonstrate the efficiency of the proposed protocols, we built a proof of concept using emerging tools. The results show that generating proofs for local training and aggregation in VerifBFL takes less than 81s and 2s, respectively, while verifying them on-chain takes less than 0.6s.
Navigating the Designs of Privacy-Preserving Fine-tuning for Large Language Models
Venue: arXiv cs.LG 2025
Year: 2025

Instruction tuning has proven effective in enhancing Large Language Models' (LLMs) performance on downstream tasks. However, real-world fine-tuning faces inherent conflicts between model providers' intellectual property protection, clients' data privacy requirements, and tuning costs. While recent approaches like split learning and offsite tuning demonstrate promising architectures for privacy-preserving fine-tuning, there is a gap in systematically addressing the multidimensional trade-offs required for diverse real-world deployments. We propose several indicative evaluation metrics to guide design trade-offs for privacy-preserving fine-tuning and a series of example designs, collectively named GuardedTuning; they result from novel combinations of system architectures with adapted privacy-enhancement methods and emerging computation techniques. Each design represents distinct trade-offs across model utility, privacy guarantees, and costs. Experimental results demonstrate that these designs protect against data reconstruction attacks while maintaining competitive fine-tuning performance.
An Efficient Adaptive Compression Method for Human Perception and Machine Vision Tasks
Venue: arXiv cs.CV 2025
Year: 2025

While most existing neural image compression (NIC) and neural video compression (NVC) methodologies have achieved remarkable success, their optimization is primarily focused on human visual perception. However, with the rapid development of artificial intelligence, many images and videos will be used for various machine vision tasks. Consequently, such existing compression methodologies cannot achieve competitive performance in machine vision. In this work, we introduce an efficient adaptive compression (EAC) method tailored for both human perception and multiple machine vision tasks. Our method involves two key modules: 1), an adaptive compression mechanism, that adaptively selects several subsets from latent features to balance the optimizations for multiple machine vision tasks (e.g., segmentation, and detection) and human vision. 2), a task-specific adapter, that uses the parameter-efficient delta-tuning strategy to stimulate the comprehensive downstream analytical networks for specific machine vision tasks. By using the above two modules, we can optimize the bit-rate costs and improve machine vision performance. In general, our proposed EAC can seamlessly integrate with existing NIC (i.e., Ballé2018, and Cheng2020) and NVC (i.e., DVC, and FVC) methods. Extensive evaluation on various benchmark datasets (i.e., VOC2007, ILSVRC2012, VOC2012, COCO, UCF101, and DAVIS) shows that our method enhances performance for multiple machine vision tasks while maintaining the quality of human vision.
On Domain Decomposition for Magnetostatic Problems in 3D
Venue: arXiv cs.CE 2025
Year: 2025

The simulation of three dimensional magnetostatic problems plays an important role, for example when simulating synchronous electric machines. Building on prior work that developed a domain decomposition algorithm using isogeometric analysis, this paper extends the method to support subdomains composed of multiple patches. This extension enables load-balancing across available CPUs, facilitated by graph partitioning tools such as METIS. The proposed approach enhances scalability and flexibility, making it suitable for large-scale simulations in diverse industrial contexts.
Sustainability in Telecommunication Networks and Key Value Indicators: a Survey
Venue: arXiv cs.NI 2025
Year: 2025

Telecommunication technologies are important enablers for both digital and ecological transitions. By offering digital alternatives to traditional modes of transportation and communication, they help reduce carbon footprints while improving access to fundamental services. Particularly in rural and remote areas, telecommunications facilitate access to education, healthcare, and employment, helping to bridge the digital divide. Additionally, telecommunications can promote sustainability by supporting renewable energy usage, gender equality, and circular economies. However, defining the role of telecommunications in sustainability remains complex due to the historical focus on performance rather than long-term societal goals. Given the significance of this theme, this paper aims to provide the reader with a deeper look at the concept of sustainability within the telecommunications sector by examining relevant initiatives and projects. It reviews the major approaches for measuring sustainability and outlines practical approaches for implementing these assessments. Furthermore, the paper explores the proposed network architectures that incorporate Key Value Indicators and discusses major technologies in this area, such as Network Digital Twins and Intent-Based Networking. Through this analysis, the paper aims to contribute to creating sustainable telecommunication networks and broader industries.
Towards accurate and reliable ICU outcome prediction: a multimodal learning framework based on belief function theory using structured EHRs and free-text notes
Venue: arXiv cs.IT 2025
Year: 2025

Accurate Intensive Care Unit (ICU) outcome prediction is critical for improving patient treatment quality and ICU resource allocation. Existing research mainly focuses on structured data, e.g. demographics and vital signs, and lacks effective frameworks to integrate clinical notes from heterogeneous electronic health records (EHRs). This study aims to explore a multimodal framework based on belief function theory that can effectively fuse heterogeneous structured EHRs and free-text notes for accurate and reliable ICU outcome prediction. The fusion strategy accounts for prediction uncertainty within each modality and conflicts between multimodal data. The experiments on MIMIC-III dataset show that our framework provides more accurate and reliable predictions than existing approaches. Specifically, it outperformed the best baseline by 1.05%/1.02% in BACC, 9.74%/6.04% in F1 score, 1.28%/0.9% in AUROC, and 6.21%/2.68% in AUPRC for predicting mortality and PLOS, respectively. Additionally, it improved the reliability of the predictions with a 26.8%/15.1% reduction in the Brier score and a 25.0%/13.3% reduction in negative log-likelihood. By effectively reducing false positives, the model can aid in better allocation of medical resources in the ICU. Furthermore, the proposed method is very versatile and can be extended to analyzing multimodal EHRs for other clinical tasks. The code implementation is available on https://github.com/yuchengruan/evid_multimodal_ehr.
iFADIT: Invertible Face Anonymization via Disentangled Identity Transform
Venue: arXiv cs.CV 2025
Year: 2025

Face anonymization aims to conceal the visual identity of a face to safeguard the individual's privacy. Traditional methods like blurring and pixelation can largely remove identifying features, but these techniques significantly degrade image quality and are vulnerable to deep reconstruction attacks. Generative models have emerged as a promising solution for anonymizing faces while preserving a natural appearance. However, many still face limitations in visual quality and often overlook the potential to recover the original face from the anonymized version, which can be valuable in specific contexts such as image forensics. This paper proposes a novel framework named iFADIT, an acronym for Invertible Face Anonymization via Disentangled Identity Transform. The framework features a disentanglement architecture coupled with a secure flow-based model: the former decouples identity information from non-identifying attributes, while the latter transforms the decoupled identity into an anonymized version in an invertible manner controlled by a secret key. The anonymized face can then be reconstructed based on a pre-trained StyleGAN that ensures high image quality and realistic facial details. Recovery of the original face (aka de-anonymization) is possible upon the availability of the matching secret, by inverting the anonymization process based on the same set of model parameters. Furthermore, a dedicated secret-key mechanism along with a dual-phase training strategy is devised to ensure the desired properties of face anonymization. Qualitative and quantitative experiments demonstrate the superiority of the proposed approach in anonymity, reversibility, security, diversity, and interpretability over competing methods.
Modern Hardware Security: A Review of Attacks and Countermeasures
Venue: arXiv cs.CR 2025
Year: 2025

With the exponential rise in the use of cloud services, smart devices, and IoT devices, advanced cyber attacks have become increasingly sophisticated and ubiquitous. Furthermore, the rapid evolution of computing architectures and memory technologies has created an urgent need to understand and address hardware security vulnerabilities. In this paper, we review the current state of vulnerabilities and mitigation strategies in contemporary computing systems. We discuss cache side-channel attacks (including Spectre and Meltdown), power side-channel attacks (such as Simple Power Analysis, Differential Power Analysis, Correlation Power Analysis, and Template Attacks), and advanced techniques like Voltage Glitching and Electromagnetic Analysis to help understand and build robust cybersecurity defense systems and guide further research. We also examine memory encryption, focusing on confidentiality, granularity, key management, masking, and re-keying strategies. Additionally, we cover Cryptographic Instruction Set Architectures, Secure Boot, Root of Trust mechanisms, Physical Unclonable Functions, and hardware fault injection techniques. The paper concludes with an analysis of the RISC-V architecture's unique security challenges. The comprehensive analysis presented in this paper is essential for building resilient hardware security solutions that can protect against both current and emerging threats in an increasingly challenging security landscape.
Rising Rested MAB with Linear Drift
Venue: arXiv cs.LG 2025
Year: 2025

We consider non-stationary multi-arm bandit (MAB) where the expected reward of each action follows a linear function of the number of times we executed the action. Our main result is a tight regret bound of $\tildeΘ(T^{4/5}K^{3/5})$, by providing both upper and lower bounds. We extend our results to derive instance dependent regret bounds, which depend on the unknown parametrization of the linear drift of the rewards.
User Simulation in the Era of Generative AI: User Modeling, Synthetic Data Generation, and System Evaluation
Venue: arXiv cs.AI 2025
Year: 2025

User simulation is an emerging interdisciplinary topic with multiple critical applications in the era of Generative AI. It involves creating an intelligent agent that mimics the actions of a human user interacting with an AI system, enabling researchers to model and analyze user behaviour, generate synthetic data for training, and evaluate interactive AI systems in a controlled and reproducible manner. User simulation has profound implications for diverse fields and plays a vital role in the pursuit of Artificial General Intelligence. This paper provides an overview of user simulation, highlighting its key applications, connections to various disciplines, and outlining future research directions to advance this increasingly important technology.
End-to-End Bangla AI for Solving Math Olympiad Problem Benchmark: Leveraging Large Language Model Using Integrated Approach
Venue: arXiv cs.CL 2025
Year: 2025

This work introduces systematic approach for enhancing large language models (LLMs) to address Bangla AI mathematical challenges. Through the assessment of diverse LLM configurations, fine-tuning with specific datasets, and the implementation of Retrieval-Augmented Generation (RAG), we enhanced the model's reasoning precision in a multilingual setting. Crucial discoveries indicate that customized prompting, dataset augmentation, and iterative reasoning improve the model's efficiency regarding Olympiad-level mathematical challenges.
Demystification and Near-perfect Estimation of Minimum Gas Limit and Gas Used for Ethereum Smart Contracts
Venue: arXiv cs.SE 2025
Year: 2025

The Ethereum blockchain has a \emph{gas system} that associates operations with a cost in gas units. Two central concepts of this system are the \emph{gas limit} assigned by the issuer of a transaction and the \emph{gas used} by a transaction. The former is a budget that must not be exhausted before the completion of the transaction execution; otherwise, the execution fails. Therefore, it seems rather essential to determine the \emph{minimum gas limit} that ensures the execution of a transaction will not abort due to the lack of gas. Despite its practical relevance, this concept has not been properly addressed. In the literature, gas used and minimum gas limit are conflated. This paper proposes a precise notion of minimum gas limit and how it can differ from gas used by a transaction; this is also demonstrated with a quantitative study on real transactions of the Ethereum blockchain. Another significant contribution is the proposition of a fairly precise estimator for each of the two metrics. Again, the confusion between these concepts has led to the creation of estimators only for the gas used by a transaction. We demonstrate that the minimum gas limit for the state of the Ethereum blockchain (after the block) $t$ can serve as a near-perfect estimation for the execution of the transaction at block $t + Δ$, where $Δ\leq 11$; the same holds for estimating gas used. These precise estimators can be very valuable in helping the users predict the gas budget of transactions and developers in optimising their smart contracts; over and underestimating gas used and minimum gas limit can lead to a number of practical issues. Overall, this paper serves as an important reference for blockchain developers and users as to how the gas system really works.
Effective Two-Stage Double Auction for Dynamic Resource Provision over Edge Networks via Discovering The Power of Overbooking
Venue: arXiv cs.GT 2025
Year: 2025

To facilitate responsive and cost-effective computing service delivery over edge networks, this paper investigates a novel two-stage double auction methodology via discovering an interesting idea of resource overbooking to overcome dynamic and uncertain nature of supply of edge servers (sellers) and demand generated from mobile devices (as buyers). The proposed auction integrates multiple essential goals such as maximizing social welfare as well as accelerating the decision-making process from both short-term and long-term views, (e.g., the time for determining winning seller-buyer pairs), by introducing a stagewise strategy: an overbooking-driven pre-double auction (OPDAuction) for determining long-term cooperations between sellers and buyers before practical resource transactions as Stage I, and a real-time backup double auction (RBDAuction) for quickly coping with residual resource demands during actual transactions. In particular, by embedding a proper overbooking rate, OPDAuction helps with facilitating trading contracts between appropriate sellers and buyers as guidance for future transactions, by allowing the booked resources to exceed theoretical supply. Then, since pre-auctions may cause risks, our RBDAuction adjusts to real-time market changes, further enhancing the overall social welfare. More importantly, we offer an interesting view to show that our proposed two-stage auction can support significant design properties such as truthfulness, individual rationality, and budget balance. Through extensive experiments, we demonstrate good performance in social welfare, time efficiency, and computational scalability, outstripping conventional methods in dynamic edge computing settings.
Multichannel Steganography: A Provably Secure Hybrid Steganographic Model for Secure Communication
Venue: arXiv cs.CR 2025
Year: 2025

Secure covert communication in hostile environments requires simultaneously achieving invisibility, provable security guarantees, and robustness against informed adversaries. This paper presents a novel hybrid steganographic framework that unites cover synthesis and cover modification within a unified multichannel protocol. A secret-seeded PRNG drives a lightweight Markov-chain generator to produce contextually plausible cover parameters, which are then masked with the payload and dispersed across independent channels. The masked bit-vector is imperceptibly embedded into conventional media via a variance-aware least-significant-bit algorithm, ensuring that statistical properties remain within natural bounds. We formalize a multichannel adversary model (MC-ATTACK) and prove that, under standard security assumptions, the adversary's distinguishing advantage is negligible, thereby guaranteeing both confidentiality and integrity. Empirical results corroborate these claims: local-variance-guided embedding yields near-lossless extraction (mean BER $<5\times10^{-3}$, correlation $>0.99$) with minimal perceptual distortion (PSNR $\approx100$,dB, SSIM $>0.99$), while key-based masking drives extraction success to zero (BER $\approx0.5$) for a fully informed adversary. Comparative analysis demonstrates that purely distortion-free or invertible schemes fail under the same threat model, underscoring the necessity of hybrid designs. The proposed approach advances high-assurance steganography by delivering an efficient, provably secure covert channel suitable for deployment in high-surveillance networks.
Improving Image Captioning by Mimicking Human Reformulation Feedback at Inference-time
Venue: arXiv cs.CV 2025
Year: 2025

Incorporating automatically predicted human feedback into the process of training generative models has attracted substantial recent interest, while feedback at inference time has received less attention. The typical feedback at training time, i.e., preferences of choice given two samples, does not naturally transfer to the inference phase. We introduce a novel type of feedback -- caption reformulations -- and train models to mimic reformulation feedback based on human annotations. Our method does not require training the image captioning model itself, thereby demanding substantially less computational effort. We experiment with two types of reformulation feedback: first, we collect a dataset of human reformulations that correct errors in the generated captions. We find that incorporating reformulation models trained on this data into the inference phase of existing image captioning models results in improved captions, especially when the original captions are of low quality. We apply our method to non-English image captioning, a domain where robust models are less prevalent, and gain substantial improvement. Second, we apply reformulations to style transfer. Quantitative evaluations reveal state-of-the-art performance on German image captioning and English style transfer, while human validation with a detailed comparative framework exposes the specific axes of improvement.